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A Structured Methodology to Assess Safety Signal Strength and Inform Causality Assessment
Causality assessment of safety signals observed with medicinal products is a foundational element of pharmacovigilance and regulatory practice, typically performed by a global introspection process. We have developed a novel, structured methodological framework to support the global introspection pr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334375/ https://www.ncbi.nlm.nih.gov/pubmed/35781676 http://dx.doi.org/10.1007/s40290-022-00436-w |
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author | Sullivan, Tim Nord, Magnus Domalik, Doug Ysander, Magnus Hermann, Richard P. |
author_facet | Sullivan, Tim Nord, Magnus Domalik, Doug Ysander, Magnus Hermann, Richard P. |
author_sort | Sullivan, Tim |
collection | PubMed |
description | Causality assessment of safety signals observed with medicinal products is a foundational element of pharmacovigilance and regulatory practice, typically performed by a global introspection process. We have developed a novel, structured methodological framework to support the global introspection process for safety signal causality assessment. This Signal Assessment Guide (SAGe) tool was developed by AstraZeneca and is used internally, both to assess safety signal strength and to inform causality decisions related to safety signals. The term ‘safety signal’ refers to information arising from one or multiple sources, which suggests a new potentially causal association, or a new aspect of a known association, between an intervention and an adverse event. The key concept underlying the SAGe tool is that safety signal data can be reliably sorted into one of three categories: aggregate safety data, plausibility data, and case-level data. When applying the tool, an evidence grade score (Levels A, B, C, and D) is transparently assigned to the available data in each category. This information can then be summarised and presented for formal decision making regarding causality for safety signals. By using a transparent method to categorise the grade of evidence for causal association, with an option to additionally derive a quantitative strength of safety signal score, the SAGe tool can support the global introspection process for causality decisions, contributing to the quality of safety information for medicinal products provided to healthcare professionals and patients. Our anecdotal experience of using the SAGe tool at AstraZeneca is that it has resulted in more efficient and robust conversations regarding the strength of safety signals and the causality question. Wider use of the SAGe tool may bring increased levels of transparency and consistency to the evaluation of safety signals. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40290-022-00436-w. |
format | Online Article Text |
id | pubmed-9334375 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-93343752022-07-30 A Structured Methodology to Assess Safety Signal Strength and Inform Causality Assessment Sullivan, Tim Nord, Magnus Domalik, Doug Ysander, Magnus Hermann, Richard P. Pharmaceut Med Current Opinion Causality assessment of safety signals observed with medicinal products is a foundational element of pharmacovigilance and regulatory practice, typically performed by a global introspection process. We have developed a novel, structured methodological framework to support the global introspection process for safety signal causality assessment. This Signal Assessment Guide (SAGe) tool was developed by AstraZeneca and is used internally, both to assess safety signal strength and to inform causality decisions related to safety signals. The term ‘safety signal’ refers to information arising from one or multiple sources, which suggests a new potentially causal association, or a new aspect of a known association, between an intervention and an adverse event. The key concept underlying the SAGe tool is that safety signal data can be reliably sorted into one of three categories: aggregate safety data, plausibility data, and case-level data. When applying the tool, an evidence grade score (Levels A, B, C, and D) is transparently assigned to the available data in each category. This information can then be summarised and presented for formal decision making regarding causality for safety signals. By using a transparent method to categorise the grade of evidence for causal association, with an option to additionally derive a quantitative strength of safety signal score, the SAGe tool can support the global introspection process for causality decisions, contributing to the quality of safety information for medicinal products provided to healthcare professionals and patients. Our anecdotal experience of using the SAGe tool at AstraZeneca is that it has resulted in more efficient and robust conversations regarding the strength of safety signals and the causality question. Wider use of the SAGe tool may bring increased levels of transparency and consistency to the evaluation of safety signals. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40290-022-00436-w. Springer International Publishing 2022-07-04 2022 /pmc/articles/PMC9334375/ /pubmed/35781676 http://dx.doi.org/10.1007/s40290-022-00436-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Current Opinion Sullivan, Tim Nord, Magnus Domalik, Doug Ysander, Magnus Hermann, Richard P. A Structured Methodology to Assess Safety Signal Strength and Inform Causality Assessment |
title | A Structured Methodology to Assess Safety Signal Strength and Inform Causality Assessment |
title_full | A Structured Methodology to Assess Safety Signal Strength and Inform Causality Assessment |
title_fullStr | A Structured Methodology to Assess Safety Signal Strength and Inform Causality Assessment |
title_full_unstemmed | A Structured Methodology to Assess Safety Signal Strength and Inform Causality Assessment |
title_short | A Structured Methodology to Assess Safety Signal Strength and Inform Causality Assessment |
title_sort | structured methodology to assess safety signal strength and inform causality assessment |
topic | Current Opinion |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334375/ https://www.ncbi.nlm.nih.gov/pubmed/35781676 http://dx.doi.org/10.1007/s40290-022-00436-w |
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